Datasets:
Reproducing the Entity Transcription Benchmark
This harness runs the benchmark end to end against any speech-to-text endpoint and scores it with the same matcher we used for our published numbers. If you follow the protocol below, your number for a given system is directly comparable to ours.
pip install -r requirements.txt
matcher_v5.py already lives in this repo under scripts/. If you are running
from a copy of the scripts folder it is already beside you; otherwise:
huggingface-cli download modulate/entity-transcription-benchmark scripts/matcher_v5.py \
--repo-type dataset --local-dir .
Run it
The harness is vendor-neutral. It needs an endpoint, the name of an environment variable holding your key, and where the transcript sits in the JSON response. Nothing about it is specific to any one provider.
export MY_STT_KEY=...
python run_benchmark.py \
--endpoint https://api.example.com/v1/transcribe \
--api-key-env MY_STT_KEY \
--response-path result.transcript \
--system-name acme-stt-v2 \
--out runs/acme.jsonl
python score_benchmark.py --hyps runs/acme.jsonl
Options for the generic path, all passed as --provider-opt k=v:
| Option | Default | Purpose |
|---|---|---|
send |
body |
body posts raw bytes; multipart posts a file field |
field_name |
file |
multipart field name |
content_type |
audio/wav |
request content type for send=body |
auth_header |
Authorization: Bearer {key} |
header template; {key} is substituted |
response_path |
text |
dotted path to the transcript, list indices allowed |
Endpoints needing no key work too — omit --api-key-env.
Convenience adapters exist for three vendors so you do not have to look up their response shapes. They are shortcuts, not the supported path:
export DEEPGRAM_API_KEY=...
python run_benchmark.py --provider deepgram --provider-opt model=nova-3 --out runs/nova3.jsonl
export OPENAI_API_KEY=...
python run_benchmark.py --provider openai --provider-opt model=gpt-4o-transcribe --out runs/oai.jsonl
Each adapter reads <PROVIDER>_API_KEY by default; --api-key-env overrides
that with any variable name you like. Keys are never read from argv, so they
stay out of shell history and out of the run manifest.
Collection is resumable — rerun the same command after an interruption and only
the missing clips are fetched. Add --overwrite to start a file from scratch.
The protocol
These five things determine the number. Change any of them and you are no longer comparing like with like:
| Default | Flag | |
|---|---|---|
| Entity spans scored | tier A only | --tier |
| Clips transcribed | all 2,151 | --tier on run_benchmark.py |
| Metric | span-level entity accuracy | — |
| Matcher | matcher_v5.hit, self-test must pass 49/49 |
--matcher-path |
| WER normalizer | basic |
--wer-normalizer |
| Dataset revision | latest | --revision |
Tier is per span, not per clip. entity_tiers is an array parallel to
entities, so a single clip can hold both tier A and tier B spans. Tier A spans
are discriminative; tier B spans are trivial for every modern system and wash out
the differences. The headline number counts tier A spans only.
Span-level accuracy. Each annotated entity is one trial. A clip with three
entities contributes three. Hit means matcher_v5.hit(entity, hypothesis)
returned true; clips with zero annotations are retained as negative examples and
contribute no trials.
WER is reported over every clip, regardless of tier, since the tier label belongs to the span rather than the audio.
The matcher is the benchmark. matcher_v5 does more than substring
matching: Unicode canonicalization, abbreviation aliases, spoken-form
reconstruction (numbers, years, dates) and contiguous token matching. Entity
accuracy is extremely sensitive to those rules, which is why a scorer written
from scratch will not land on our numbers. score_benchmark.py refuses to run
if matcher_v5.py is absent or its 49-case self-test fails, rather than quietly
substituting a weaker rule.
No post-processing. Hypotheses are scored exactly as the endpoint returns them. If your system has a formatting layer, benchmark it on and off and report both, but say which one the headline number used.
Pin the revision with --revision <commit-sha> for anything you publish.
Corrections to the annotations will move the numbers slightly.
Reporting
score_benchmark.py writes three files next to the hypotheses:
*.hits.csv— one row per entity span, with the hit flag and the transcript*.summary.json— headline accuracy, WER, per-type and per-subset breakdown*.manifest.json— dataset revision, tier, system name and request options
Publish the summary and the manifest together. A percentage without the manifest is not reproducible by anyone, including us.
If your number disagrees with ours, diff at the span level rather than arguing about the percentage:
python score_benchmark.py --hyps runs/mine.jsonl --compare reference/nova3.hits.csv
That writes a *.diff.csv of exactly which entities the two runs disagree on.
We would rather hear about those than about the headline.
Known limits
- Hosted models drift. Vendor endpoints change without version bumps, so a rerun months later may not match ours. Always record the model string and the run date; both go into the manifest automatically.
- Our own system is not public. Third parties can reproduce the numbers for any hosted vendor on this list, and can score their own system on the same scale, but cannot independently verify our internal result.
- Annotations are LLM-assisted. Entity type labels were generated by model passes with human adjudication of disagreements, and a residue of boundary artifacts remains. These depress every system equally, so comparisons hold, but the ground truth is not gold-standard human annotation. Corrections are welcome at ml.modulate@modulate.ai.